What is Beta Regression and how to apply it with R?

Beta regression is commonly used to model variables that have values between 0 to 1, typically when the data points of Y variable represent a proportion of individuals from a subset of the total population (assuming that it follows a beta distribution). This often addresses the problem of heteroskedasticity.

Some examples of Y variables where beta regression would be appropriate

1. From GasolineYield data: Proportion of crude oil converted to gasoline after distillation and fractionation
2. Proportion of individuals infected with ‘xyz’ when exposed to various levels of artifical preservative agent 898D.

Example: Gasoline Yield

The example below shows an example implementation of beta regression using the GasolineYield data from betareg package.